Vehicle accidents are often caused by driver drowsiness and inattention. In-vehicle cameras and sensors coupled with computer vision techniques can be employed to automatically monitor driver behavior and enhance safety and reduce accidents. In-vehicle video analytics can also be employed in the context of evidentiary support, for example, when adjudicating traffic violations or accidents. Such monitoring devices are typically found only in high-end vehicles and rely upon sophisticated image-capturing and processing afforded by specialized hardware that interacts with built-in vehicle telematics. Such systems, however, are expensive to implement and not currently very reliable.
Several approaches have been suggested for monitoring driver behavior. One technique involves, for example, the use of a mobile device such as a smartphone or portable camera that can be temporarily mounted within the vehicle for the purpose of driver monitoring. Such mobile monitoring devices can potentially offer a cost-effective service for users who do not rely upon dedicated built-in systems (e.g., systems implemented by automobile manufacturers). An inertial sensor and components such as an accelerometer, a gyroscope, and/or a magnetometer associated with the mobile device can be utilized to obtain data such as the position, speed, acceleration, deflection angle, etc., of the device and relate this information to driver behavior. Such an approach can monitor some aspects of driving behavior; however, it cannot predict significant events such as driver drowsiness, inattention, or other distractions that may lead to unsafe driving conditions.
Another approach involves the use of a mobile monitoring device placed on a windshield with a rear camera facing the road. This application can monitor the distance to nearby vehicles along with lane departure data and the vehicle speed via Global Positioning System (GPS). Such an approach however, does not capture any data about the state of the driver and thus cannot accurately monitor driver attention or fatigue. Another approach employs dual video captured from a driver-facing (or front) and road-facing (or rear) camera that automatically switches between front and rear cameras based on detection of various events. Such an approach provides only a coarse estimate of gaze and is not accurate.
Based on the foregoing, it is believed that a need exists for an improved method and system for estimating and monitoring the gaze direction of a driver in a vehicle for real time execution via a portable mobile device, as will be described in greater detail herein.